import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
				    TimesformerForVideoClassification,
				    TimesformerModel,
				)
				from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from transformers import VideoMAEImageProcessor





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     Optional[Any]=1_3     ,  snake_case__     :     List[Any]=1_0     ,  snake_case__     :     List[str]=3     ,  snake_case__     :     List[str]=2     ,  snake_case__     :     Tuple=2     ,  snake_case__     :     int=True     ,  snake_case__     :     Dict=True     ,  snake_case__     :     int=3_2     ,  snake_case__     :     Dict=5     ,  snake_case__     :     Union[str, Any]=4     ,  snake_case__     :     List[Any]=3_7     ,  snake_case__     :     Any="gelu"     ,  snake_case__     :     List[str]=0.1     ,  snake_case__     :     Dict=0.1     ,  snake_case__     :     Union[str, Any]=1_0     ,  snake_case__     :     Union[str, Any]=0.02     ,  snake_case__     :     int="divided_space_time"     ,  snake_case__     :     Tuple=None     ,  )      ->	Dict:
				_lowerCamelCase				   =  parent
				_lowerCamelCase				   =  batch_size
				_lowerCamelCase				   =  image_size
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  patch_size
				_lowerCamelCase				   =  num_frames
				_lowerCamelCase				   =  is_training
				_lowerCamelCase				   =  use_labels
				_lowerCamelCase				   =  hidden_size
				_lowerCamelCase				   =  num_hidden_layers
				_lowerCamelCase				   =  num_attention_heads
				_lowerCamelCase				   =  intermediate_size
				_lowerCamelCase				   =  hidden_act
				_lowerCamelCase				   =  hidden_dropout_prob
				_lowerCamelCase				   =  attention_probs_dropout_prob
				_lowerCamelCase				   =  attention_type
				_lowerCamelCase				   =  initializer_range
				_lowerCamelCase				   =  scope
				_lowerCamelCase				   =  num_labels

				# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
				_lowerCamelCase				   =  (image_size // patch_size) ** 2
				_lowerCamelCase				   =  (num_frames) * self.num_patches_per_frame + 1
		def   _snake_case       (       self     :     str		)      ->	int:
				_lowerCamelCase				   =  floats_tensor(
				    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]		)

				_lowerCamelCase				   =  None
				if self.use_labels:
						_lowerCamelCase				   =  ids_tensor([self.batch_size]     ,  self.num_labels		)

				_lowerCamelCase				   =  self.get_config()

				return config, pixel_values, labels
		def   _snake_case       (       self     :     List[str]		)      ->	str:
				_lowerCamelCase				   =  TimesformerConfig(
				    image_size=self.image_size     ,  patch_size=self.patch_size     ,  num_channels=self.num_channels     ,  num_frames=self.num_frames     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  initializer_range=self.initializer_range     ,  attention_type=self.attention_type     ,  )
				_lowerCamelCase				   =  self.num_labels
				return config
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Any     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]		)      ->	Optional[Any]:
				_lowerCamelCase				   =  TimesformerModel(config=snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)		)
		def   _snake_case       (       self     :     str     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     int		)      ->	List[Any]:
				_lowerCamelCase				   =  TimesformerForVideoClassification(snake_case__		)
				model.to(snake_case__		)
				model.eval()

				_lowerCamelCase				   =  model(snake_case__		)

				# verify the logits shape
				_lowerCamelCase				   =  torch.Size((self.batch_size, self.num_labels)		)
				self.parent.assertEqual(result.logits.shape     ,  snake_case__		)



		def   _snake_case       (       self     :     Tuple		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.prepare_config_and_inputs()
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  config_and_inputs
				_lowerCamelCase				   =  {'pixel_values': pixel_values}
				return config, inputs_dict





@require_torch
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
		lowerCAmelCase_     =   (
		    {'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		def   _snake_case       (       self     :     List[str]		)      ->	Optional[int]:
				_lowerCamelCase				   =  TimesformerModelTester(self		)
				_lowerCamelCase				   =  ConfigTester(
				    self     ,  config_class=snake_case__     ,  has_text_modality=snake_case__     ,  hidden_size=3_7		)
		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     int     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     List[str]=False		)      ->	Any:
				_lowerCamelCase				   =  copy.deepcopy(snake_case__		)

				if return_labels:
						if model_class in get_values(snake_case__		):
								_lowerCamelCase				   =  torch.zeros(
								    self.model_tester.batch_size     ,  dtype=torch.long     ,  device=snake_case__		)

				return inputs_dict
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	List[str]:
				self.config_tester.run_common_tests()
		@unittest.skip(reason='TimeSformer does not use inputs_embeds'		)
		def   _snake_case       (       self     :     Any		)      ->	int:
				pass
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Dict:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(snake_case__		)
						self.assertIsInstance(model.get_input_embeddings()     ,  (nn.Module)		)
						_lowerCamelCase				   =  model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(snake_case__     ,  nn.Linear		)		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Optional[int]:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(snake_case__		)
						_lowerCamelCase				   =  inspect.signature(model.forward		)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_lowerCamelCase				   =  [*signature.parameters.keys()]

						_lowerCamelCase				   =  ['pixel_values']
						self.assertListEqual(arg_names[:1]     ,  snake_case__		)
		def   _snake_case       (       self     :     Any		)      ->	int:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*snake_case__		)
		def   _snake_case       (       self     :     Any		)      ->	List[Any]:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_video_classification(*snake_case__		)
		@slow
		def   _snake_case       (       self     :     int		)      ->	int:
				for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowerCamelCase				   =  TimesformerModel.from_pretrained(snake_case__		)
						self.assertIsNotNone(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Any:
				if not self.has_attentions:
						pass

				else:
						_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()
						_lowerCamelCase				   =  True

						for model_class in self.all_model_classes:
								_lowerCamelCase				   =  self.model_tester.seq_length
								_lowerCamelCase				   =  self.model_tester.num_frames

								_lowerCamelCase				   =  True
								_lowerCamelCase				   =  False
								_lowerCamelCase				   =  True
								_lowerCamelCase				   =  model_class(snake_case__		)
								model.to(snake_case__		)
								model.eval()
								with torch.no_grad():
										_lowerCamelCase				   =  model(**self._prepare_for_class(snake_case__     ,  snake_case__		)		)
								_lowerCamelCase				   =  outputs.attentions
								self.assertEqual(len(snake_case__		)     ,  self.model_tester.num_hidden_layers		)

								# check that output_attentions also work using config
								del inputs_dict["output_attentions"]
								_lowerCamelCase				   =  True
								_lowerCamelCase				   =  model_class(snake_case__		)
								model.to(snake_case__		)
								model.eval()
								with torch.no_grad():
										_lowerCamelCase				   =  model(**self._prepare_for_class(snake_case__     ,  snake_case__		)		)
								_lowerCamelCase				   =  outputs.attentions
								self.assertEqual(len(snake_case__		)     ,  self.model_tester.num_hidden_layers		)

								# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
								self.assertListEqual(
								    list(attentions[0].shape[-3:]		)     ,  [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]     ,  )
								_lowerCamelCase				   =  len(snake_case__		)

								# Check attention is always last and order is fine
								_lowerCamelCase				   =  True
								_lowerCamelCase				   =  True
								_lowerCamelCase				   =  model_class(snake_case__		)
								model.to(snake_case__		)
								model.eval()
								with torch.no_grad():
										_lowerCamelCase				   =  model(**self._prepare_for_class(snake_case__     ,  snake_case__		)		)

								self.assertEqual(out_len + 1     ,  len(snake_case__		)		)

								_lowerCamelCase				   =  outputs.attentions

								self.assertEqual(len(snake_case__		)     ,  self.model_tester.num_hidden_layers		)

								# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
								self.assertListEqual(
								    list(self_attentions[0].shape[-3:]		)     ,  [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]     ,  )



		def   _snake_case       (       self     :     List[Any]		)      ->	Tuple:
				def check_hidden_states_output(snake_case__     :     Any     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Tuple		):
						_lowerCamelCase				   =  model_class(snake_case__		)
						model.to(snake_case__		)
						model.eval()

						with torch.no_grad():
								_lowerCamelCase				   =  model(**self._prepare_for_class(snake_case__     ,  snake_case__		)		)

						_lowerCamelCase				   =  outputs.hidden_states
						_lowerCamelCase				   =  self.model_tester.num_hidden_layers + 1
						self.assertEqual(len(snake_case__		)     ,  snake_case__		)

						_lowerCamelCase				   =  self.model_tester.seq_length

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]		)     ,  [seq_length, self.model_tester.hidden_size]     ,  )

				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  True
						check_hidden_states_output(snake_case__     ,  snake_case__     ,  snake_case__		)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						_lowerCamelCase				   =  True

						check_hidden_states_output(snake_case__     ,  snake_case__     ,  snake_case__		)



def 	lowerCamelCase					(	)   ->    str:
		_lowerCamelCase				   =  hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video'			,       filename='eating_spaghetti.npy'			,       repo_type='dataset'							)
		_lowerCamelCase				   =  np.load(UpperCamelCase							)
		return list(UpperCamelCase							)





@require_torch
@require_vision
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		@cached_property
		def   _snake_case       (       self     :     Any		)      ->	Dict:
				# logits were tested with a different mean and std, so we use the same here
				return (
				    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,  image_std=[0.5, 0.5, 0.5]		)
				    if is_vision_available()
				    else None
				)



		@slow
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				_lowerCamelCase				   =  TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'		).to(
				    snake_case__		)

				_lowerCamelCase				   =  self.default_image_processor
				_lowerCamelCase				   =  prepare_video()
				_lowerCamelCase				   =  image_processor(video[:8]     ,  return_tensors='pt'		).to(snake_case__		)

				# forward pass
				with torch.no_grad():
						_lowerCamelCase				   =  model(**snake_case__		)

				# verify the logits
				_lowerCamelCase				   =  torch.Size((1, 4_0_0)		)
				self.assertEqual(outputs.logits.shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor([-0.3016, -0.7713, -0.4205]		).to(snake_case__		)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,  snake_case__     ,  atol=1e-4		)		)
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.exp(UpperCamelCase							)
		_lowerCamelCase				   =  torch.sum(UpperCamelCase			,       dim=1							)  # sum of exp(x_i)
		_lowerCamelCase				   =  torch.sum(x * exp_x			,       dim=1							)  # sum of x_i * exp(x_i)
		return torch.log(UpperCamelCase							) - B / A





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()
				_lowerCamelCase				   =  config.output_attentions
				_lowerCamelCase				   =  config.output_hidden_states
				_lowerCamelCase				   =  nn.ModuleList([BertLayer(snake_case__		) for _ in range(config.num_hidden_layers		)]		)
				_lowerCamelCase				   =  nn.ModuleList([BertHighway(snake_case__		) for _ in range(config.num_hidden_layers		)]		)

				_lowerCamelCase				   =  [-1 for _ in range(config.num_hidden_layers		)]
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     str		)      ->	Optional[int]:
				if (type(snake_case__		) is float) or (type(snake_case__		) is int):
						for i in range(len(self.early_exit_entropy		)		):
								_lowerCamelCase				   =  x
				else:
						_lowerCamelCase				   =  x
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     Optional[int]		)      ->	int:
				_lowerCamelCase				   =  pooler.state_dict()
				for highway in self.highway:
						for name, param in highway.pooler.state_dict().items():
								param.copy_(loaded_model[name]		)



		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     int=None     ,  snake_case__     :     Optional[int]=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Dict=None     ,  )      ->	List[str]:
				_lowerCamelCase				   =  ()
				_lowerCamelCase				   =  ()
				_lowerCamelCase				   =  ()
				for i, layer_module in enumerate(self.layer		):
						if self.output_hidden_states:
								_lowerCamelCase				   =  all_hidden_states + (hidden_states,)

						_lowerCamelCase				   =  layer_module(
						    snake_case__     ,  snake_case__     ,  head_mask[i]     ,  snake_case__     ,  snake_case__		)
						_lowerCamelCase				   =  layer_outputs[0]

						if self.output_attentions:
								_lowerCamelCase				   =  all_attentions + (layer_outputs[1],)

						_lowerCamelCase				   =  (hidden_states,)
						if self.output_hidden_states:
								_lowerCamelCase				   =  current_outputs + (all_hidden_states,)
						if self.output_attentions:
								_lowerCamelCase				   =  current_outputs + (all_attentions,)

						_lowerCamelCase				   =  self.highway[i](snake_case__		)
						# logits, pooled_output

						if not self.training:
								_lowerCamelCase				   =  highway_exit[0]
								_lowerCamelCase				   =  entropy(snake_case__		)
								_lowerCamelCase				   =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
								_lowerCamelCase				   =  all_highway_exits + (highway_exit,)

								if highway_entropy < self.early_exit_entropy[i]:
										_lowerCamelCase				   =  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
										raise HighwayException(snake_case__     ,  i + 1		)
						else:
								_lowerCamelCase				   =  all_highway_exits + (highway_exit,)

        # Add last layer
				if self.output_hidden_states:
						_lowerCamelCase				   =  all_hidden_states + (hidden_states,)

				_lowerCamelCase				   =  (hidden_states,)
				if self.output_hidden_states:
						_lowerCamelCase				   =  outputs + (all_hidden_states,)
				if self.output_attentions:
						_lowerCamelCase				   =  outputs + (all_attentions,)

				_lowerCamelCase				   =  outputs + (all_highway_exits,)
				return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '     ,SCREAMING_SNAKE_CASE_     ,)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     List[Any]		)      ->	Any:
				super().__init__(snake_case__		)
				_lowerCamelCase				   =  config

				_lowerCamelCase				   =  BertEmbeddings(snake_case__		)
				_lowerCamelCase				   =  DeeBertEncoder(snake_case__		)
				_lowerCamelCase				   =  BertPooler(snake_case__		)

				self.init_weights()
		def   _snake_case       (       self     :     Tuple		)      ->	Tuple:
				self.encoder.init_highway_pooler(self.pooler		)
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				return self.embeddings.word_embeddings
		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     List[Any]		)      ->	Any:
				_lowerCamelCase				   =  value
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				for layer, heads in heads_to_prune.items():
						self.encoder.layer[layer].attention.prune_heads(snake_case__		)



		@add_start_docstrings_to_model_forward(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     str=None     ,  snake_case__     :     List[Any]=None     ,  )      ->	List[str]:
				if input_ids is not None and inputs_embeds is not None:
						raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'		)
				elif input_ids is not None:
						_lowerCamelCase				   =  input_ids.size()
				elif inputs_embeds is not None:
						_lowerCamelCase				   =  inputs_embeds.size()[:-1]
				else:
						raise ValueError('You have to specify either input_ids or inputs_embeds'		)

				_lowerCamelCase				   =  input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
						_lowerCamelCase				   =  torch.ones(snake_case__     ,  device=snake_case__		)
				if encoder_attention_mask is None:
						_lowerCamelCase				   =  torch.ones(snake_case__     ,  device=snake_case__		)
				if token_type_ids is None:
						_lowerCamelCase				   =  torch.zeros(snake_case__     ,  dtype=torch.long     ,  device=snake_case__		)

				# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
				# ourselves in which case we just need to make it broadcastable to all heads.
				_lowerCamelCase				   =  self.get_extended_attention_mask(snake_case__     ,  snake_case__     ,  snake_case__		)

				# If a 2D ou 3D attention mask is provided for the cross-attention
				# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
				if encoder_attention_mask.dim() == 3:
						_lowerCamelCase				   =  encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.dim() == 2:
						_lowerCamelCase				   =  encoder_attention_mask[:, None, None, :]

				_lowerCamelCase				   =  encoder_extended_attention_mask.to(
				    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
				_lowerCamelCase				   =  (1.0 - encoder_extended_attention_mask) * -1_0000.0

				# Prepare head mask if needed
				# 1.0 in head_mask indicate we keep the head
				# attention_probs has shape bsz x n_heads x N x N
				# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
				# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
				_lowerCamelCase				   =  self.get_head_mask(snake_case__     ,  self.config.num_hidden_layers		)

				_lowerCamelCase				   =  self.embeddings(
				    input_ids=snake_case__     ,  position_ids=snake_case__     ,  token_type_ids=snake_case__     ,  inputs_embeds=snake_case__		)
				_lowerCamelCase				   =  self.encoder(
				    snake_case__     ,  attention_mask=snake_case__     ,  head_mask=snake_case__     ,  encoder_hidden_states=snake_case__     ,  encoder_attention_mask=snake_case__     ,  )
				_lowerCamelCase				   =  encoder_outputs[0]
				_lowerCamelCase				   =  self.pooler(snake_case__		)

				_lowerCamelCase				   =  (
				    sequence_output,
				    pooled_output,
				) + encoder_outputs[
				    1:
				]  # add hidden_states and attentions if they are here
				return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Dict     ,  snake_case__     :     Any		)      ->	str:
				_lowerCamelCase				   =  message
				_lowerCamelCase				   =  exit_layer  # start from 1!





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]		)      ->	Any:
				super().__init__()
				_lowerCamelCase				   =  BertPooler(snake_case__		)
				_lowerCamelCase				   =  nn.Dropout(config.hidden_dropout_prob		)
				_lowerCamelCase				   =  nn.Linear(config.hidden_size     ,  config.num_labels		)



		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Dict		)      ->	Optional[Any]:
				# Pooler
				_lowerCamelCase				   =  encoder_outputs[0]
				_lowerCamelCase				   =  self.pooler(snake_case__		)
				# "return" pooler_output

				# BertModel
				_lowerCamelCase				   =  (pooler_input, pooler_output) + encoder_outputs[1:]
				# "return" bmodel_output

				# Dropout and classification
				_lowerCamelCase				   =  bmodel_output[1]

				_lowerCamelCase				   =  self.dropout(snake_case__		)
				_lowerCamelCase				   =  self.classifier(snake_case__		)

				return logits, pooled_output





@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '     ,SCREAMING_SNAKE_CASE_     ,)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Dict		)      ->	Dict:
				super().__init__(snake_case__		)
				_lowerCamelCase				   =  config.num_labels
				_lowerCamelCase				   =  config.num_hidden_layers

				_lowerCamelCase				   =  DeeBertModel(snake_case__		)
				_lowerCamelCase				   =  nn.Dropout(config.hidden_dropout_prob		)
				_lowerCamelCase				   =  nn.Linear(config.hidden_size     ,  self.config.num_labels		)

				self.init_weights()



		@add_start_docstrings_to_model_forward(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     Optional[int]=None     ,  snake_case__     :     str=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     str=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     int=None     ,  snake_case__     :     Union[str, Any]=-1     ,  snake_case__     :     Any=False     ,  )      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.num_layers
				try:
						_lowerCamelCase				   =  self.bert(
						    snake_case__     ,  attention_mask=snake_case__     ,  token_type_ids=snake_case__     ,  position_ids=snake_case__     ,  head_mask=snake_case__     ,  inputs_embeds=snake_case__     ,  )
						# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

						_lowerCamelCase				   =  outputs[1]

						_lowerCamelCase				   =  self.dropout(snake_case__		)
						_lowerCamelCase				   =  self.classifier(snake_case__		)
						_lowerCamelCase				   =  (logits,) + outputs[2:]  # add hidden states and attention if they are here
				except HighwayException as e:
						_lowerCamelCase				   =  e.message
						_lowerCamelCase				   =  e.exit_layer
						_lowerCamelCase				   =  outputs[0]

				if not self.training:
						_lowerCamelCase				   =  entropy(snake_case__		)
						_lowerCamelCase				   =  []
						_lowerCamelCase				   =  []
				if labels is not None:
						if self.num_labels == 1:
								#  We are doing regression
								_lowerCamelCase				   =  MSELoss()
								_lowerCamelCase				   =  loss_fct(logits.view(-1		)     ,  labels.view(-1		)		)
						else:
								_lowerCamelCase				   =  CrossEntropyLoss()
								_lowerCamelCase				   =  loss_fct(logits.view(-1     ,  self.num_labels		)     ,  labels.view(-1		)		)

						# work with highway exits
						_lowerCamelCase				   =  []
						for highway_exit in outputs[-1]:
								_lowerCamelCase				   =  highway_exit[0]
								if not self.training:
										highway_logits_all.append(snake_case__		)
										highway_entropy.append(highway_exit[2]		)
								if self.num_labels == 1:
										#  We are doing regression
										_lowerCamelCase				   =  MSELoss()
										_lowerCamelCase				   =  loss_fct(highway_logits.view(-1		)     ,  labels.view(-1		)		)
								else:
										_lowerCamelCase				   =  CrossEntropyLoss()
										_lowerCamelCase				   =  loss_fct(highway_logits.view(-1     ,  self.num_labels		)     ,  labels.view(-1		)		)
								highway_losses.append(snake_case__		)

						if train_highway:
								_lowerCamelCase				   =  (sum(highway_losses[:-1]		),) + outputs
								# exclude the final highway, of course
						else:
								_lowerCamelCase				   =  (loss,) + outputs
				if not self.training:
						_lowerCamelCase				   =  outputs + ((original_entropy, highway_entropy), exit_layer)
						if output_layer >= 0:
								_lowerCamelCase				   =  (
								    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
								)  # use the highway of the last layer

				return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)